{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#男生体测数据\n",
    "test_boy = pd.read_excel('./体测分数_男生.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>477.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>10.423480</td>\n",
       "      <td>3.716478</td>\n",
       "      <td>71.953878</td>\n",
       "      <td>7.807966</td>\n",
       "      <td>72.094340</td>\n",
       "      <td>211.461216</td>\n",
       "      <td>67.440252</td>\n",
       "      <td>12.188679</td>\n",
       "      <td>73.507338</td>\n",
       "      <td>7.893082</td>\n",
       "      <td>55.574423</td>\n",
       "      <td>4311.371069</td>\n",
       "      <td>85.624738</td>\n",
       "      <td>169.964361</td>\n",
       "      <td>62.188679</td>\n",
       "      <td>20.520797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>4.867673</td>\n",
       "      <td>0.917703</td>\n",
       "      <td>19.946198</td>\n",
       "      <td>1.503325</td>\n",
       "      <td>15.087891</td>\n",
       "      <td>38.873111</td>\n",
       "      <td>16.924421</td>\n",
       "      <td>6.189415</td>\n",
       "      <td>16.780167</td>\n",
       "      <td>4.679552</td>\n",
       "      <td>30.245168</td>\n",
       "      <td>1106.008477</td>\n",
       "      <td>19.551259</td>\n",
       "      <td>26.774517</td>\n",
       "      <td>14.646196</td>\n",
       "      <td>4.620515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>3765.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>55.400002</td>\n",
       "      <td>18.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.030000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>4340.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>20.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>4970.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>22.559999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.470000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.020000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>7852.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>113.500000</td>\n",
       "      <td>39.270000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               班级     男1000米跑   男1000米跑分数       男50米跑     男50米跑分数         男跳远  \\\n",
       "count  477.000000  477.000000  477.000000  477.000000  477.000000  477.000000   \n",
       "mean    10.423480    3.716478   71.953878    7.807966   72.094340  211.461216   \n",
       "std      4.867673    0.917703   19.946198    1.503325   15.087891   38.873111   \n",
       "min      1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      7.000000    3.480000   68.000000    7.670000   70.000000  202.000000   \n",
       "50%     11.000000    4.010000   76.000000    8.030000   74.000000  215.000000   \n",
       "75%     14.000000    4.160000   80.000000    8.300000   76.000000  230.000000   \n",
       "max     17.000000    5.470000  100.000000   10.020000  100.000000  295.000000   \n",
       "\n",
       "            男跳远分数        男体前屈      男体前屈分数         男引体       男引体分数  \\\n",
       "count  477.000000  477.000000  477.000000  477.000000  477.000000   \n",
       "mean    67.440252   12.188679   73.507338    7.893082   55.574423   \n",
       "std     16.924421    6.189415   16.780167    4.679552   30.245168   \n",
       "min      0.000000   -5.000000    0.000000    0.000000    0.000000   \n",
       "25%     62.000000    8.000000   70.000000    4.000000   30.000000   \n",
       "50%     70.000000   12.000000   74.000000    8.000000   64.000000   \n",
       "75%     76.000000   16.000000   80.000000   11.000000   76.000000   \n",
       "max    100.000000   31.000000  100.000000   23.000000  100.000000   \n",
       "\n",
       "              男肺活量      男肺活量分数          身高          体重         BMI  \n",
       "count   477.000000  477.000000  477.000000  477.000000  477.000000  \n",
       "mean   4311.371069   85.624738  169.964361   62.188679   20.520797  \n",
       "std    1106.008477   19.551259   26.774517   14.646196    4.620515  \n",
       "min       0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%    3765.000000   78.000000  170.000000   55.400002   18.510000  \n",
       "50%    4340.000000   90.000000  174.000000   61.500000   20.180000  \n",
       "75%    4970.000000  100.000000  178.000000   69.000000   22.559999  \n",
       "max    7852.000000  100.000000  196.000000  113.500000   39.270000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_boy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#女生体测数据\n",
    "test_girl = pd.read_excel('./体测分数_女生.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>593.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>8.413153</td>\n",
       "      <td>30.830135</td>\n",
       "      <td>72.819562</td>\n",
       "      <td>9.688820</td>\n",
       "      <td>60.131535</td>\n",
       "      <td>159.750422</td>\n",
       "      <td>67.522766</td>\n",
       "      <td>15.871838</td>\n",
       "      <td>76.849916</td>\n",
       "      <td>38.266442</td>\n",
       "      <td>74.075885</td>\n",
       "      <td>3010.323777</td>\n",
       "      <td>85.365936</td>\n",
       "      <td>159.677066</td>\n",
       "      <td>54.336425</td>\n",
       "      <td>20.810978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>4.498832</td>\n",
       "      <td>298.368640</td>\n",
       "      <td>21.338476</td>\n",
       "      <td>1.870731</td>\n",
       "      <td>17.999331</td>\n",
       "      <td>28.950186</td>\n",
       "      <td>15.077947</td>\n",
       "      <td>5.409304</td>\n",
       "      <td>13.842724</td>\n",
       "      <td>8.115707</td>\n",
       "      <td>13.186269</td>\n",
       "      <td>731.084283</td>\n",
       "      <td>15.985403</td>\n",
       "      <td>18.284695</td>\n",
       "      <td>9.902976</td>\n",
       "      <td>3.662867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>2577.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>49.700001</td>\n",
       "      <td>19.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>9.960000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2987.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>20.469999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>4.040000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>10.340000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3463.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>58.599998</td>\n",
       "      <td>22.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4012.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>13.900000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>92.800003</td>\n",
       "      <td>36.090000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               班级       女800米跑    女800米跑分数       女50米跑     女50米跑分数  \\\n",
       "count  593.000000   593.000000  593.000000  593.000000  593.000000   \n",
       "mean     8.413153    30.830135   72.819562    9.688820   60.131535   \n",
       "std      4.498832   298.368640   21.338476    1.870731   17.999331   \n",
       "min      1.000000     0.000000    0.000000    0.000000    0.000000   \n",
       "25%      5.000000     3.400000   72.000000    9.550000   60.000000   \n",
       "50%      8.000000     3.520000   78.000000    9.960000   66.000000   \n",
       "75%     12.000000     4.040000   85.000000   10.340000   68.000000   \n",
       "max     17.000000  4012.000000  100.000000   13.900000  100.000000   \n",
       "\n",
       "              女跳远       女跳远分数        女体前屈      女体前屈分数         女仰卧       女仰卧分数  \\\n",
       "count  593.000000  593.000000  593.000000  593.000000  593.000000  593.000000   \n",
       "mean   159.750422   67.522766   15.871838   76.849916   38.266442   74.075885   \n",
       "std     28.950186   15.077947    5.409304   13.842724    8.115707   13.186269   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%    152.000000   62.000000   13.000000   72.000000   35.000000   72.000000   \n",
       "50%    162.000000   68.000000   16.000000   76.000000   40.000000   76.000000   \n",
       "75%    171.000000   74.000000   19.000000   80.000000   43.000000   80.000000   \n",
       "max    220.000000  100.000000   30.000000  100.000000   58.000000  100.000000   \n",
       "\n",
       "              女肺活量      女肺活量分数          身高          体重         BMI  \n",
       "count   593.000000  593.000000  593.000000  593.000000  593.000000  \n",
       "mean   3010.323777   85.365936  159.677066   54.336425   20.810978  \n",
       "std     731.084283   15.985403   18.284695    9.902976    3.662867  \n",
       "min       0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%    2577.000000   76.000000  158.000000   49.700001   19.070000  \n",
       "50%    2987.000000   85.000000  162.000000   53.500000   20.469999  \n",
       "75%    3463.000000  100.000000  165.000000   58.599998   22.410000  \n",
       "max    6435.000000  100.000000  178.000000   92.800003   36.090000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_girl.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#男1000米跑\n",
    "cond = test_boy.iloc[:,2]>0\n",
    "boy_run = test_boy.iloc[:,2][cond]\n",
    "boy_run = pd.cut(boy_run,bins=3,labels=['合格','优秀','不合格'])\n",
    "data1 = boy_run.value_counts()\n",
    "\n",
    "#男引体\n",
    "boy_pull = test_boy.iloc[:,10]\n",
    "boy_pull = pd.cut(boy_pull,bins=3,labels=['不合格','合格','优秀'])\n",
    "data2 = boy_pull.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '  男引体分布情况  ')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "gs = gridspec.GridSpec(1,2)#10行的图\n",
    "\n",
    "plt.subplot(gs[0])\n",
    "plt.pie(data1,labels=['合格','优秀','不合格'],\n",
    "        textprops={'family':'Kaiti','fontsize':15},\n",
    "       autopct='%.2f%%',\n",
    "        shadow=True)\n",
    "plt.title('  男1000米跑分布情况  ',fontsize=20)\n",
    "\n",
    "plt.subplot(gs[1])\n",
    "_ = plt.pie(data2,labels=['不合格','合格','优秀'],\n",
    "            textprops={'family':'Kaiti','fontsize':15},\n",
    "            autopct='%.2f%%',\n",
    "           shadow=True)\n",
    "plt.title('  男引体分布情况  ',fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '  女800米跑分数分布情况  ')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#女800米跑\n",
    "plt.figure(figsize=(9,6))\n",
    "girl_run = test_girl.iloc[:,3]\n",
    "girl_run\n",
    "plt.hist(girl_run,bins = 4,density=True)\n",
    "\n",
    "plt.box(False)\n",
    "\n",
    "plt.grid(axis='y',color = '#3c7f99')\n",
    "\n",
    "plt.title('  女800米跑分数分布情况  ',fontsize=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '  女跳远分数分布情况  ')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#女跳远\n",
    "plt.figure(figsize=(9,6))\n",
    "girl_jump = test_girl.iloc[:,7]\n",
    "girl_jump\n",
    "plt.hist(girl_jump,bins = 4,density=True)\n",
    "\n",
    "plt.box(False)\n",
    "\n",
    "plt.grid(axis='y',color = '#3c7f99')\n",
    "\n",
    "plt.title('  女跳远分数分布情况  ',fontsize=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第三题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "boy_BMI = pd.cut(test_boy.iloc[:,-1],\n",
    "                  bins=[0,14.3,21.4,24.1,40],\n",
    "                  labels=['低体重','正常','超重','肥胖'])\n",
    "boy_BMI_data = boy_BMI.value_counts()\n",
    "\n",
    "girl_BMI = pd.cut(test_girl.iloc[:,-1],\n",
    "                  bins=[0,16.4,22.7,25.2,40],\n",
    "                  labels=['低体重','正常','超重','肥胖'])\n",
    "girl_BMI_data = girl_BMI.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x905b848>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "#男BMI 外圈\n",
    "plt.pie(boy_BMI_data,radius=1,#设置半径为1\n",
    "       autopct='%0.2f%%',\n",
    "        colors= [\"c\",\"orange\",\"yellow\",\"red\"],\n",
    "       wedgeprops={'linewidth':3,'width':0.3,'edgecolor':'white'},#间距的宽度，饼图的宽度，间隔的颜色\n",
    "       pctdistance=0.85,#文字位置\n",
    "          labels=['低体重','正常','超重','肥胖'],\n",
    "          textprops={'family':'Kaiti','fontsize':15}\n",
    "         )\n",
    "\n",
    "#女BMI 内圈\n",
    "_=plt.pie(girl_BMI_data,radius=0.7,autopct='%0.2f%%',pctdistance=0.55,\n",
    "        wedgeprops={'linewidth':3,'width':0.7,'edgecolor':'white'},\n",
    "         colors= [\"c\",\"orange\",\"yellow\",\"red\"],\n",
    "         textprops={'family':'Kaiti','fontsize':15})\n",
    "\n",
    "plt.title('男女生体重指数统计',fontsize=30)\n",
    "\n",
    "plt.rcParams['font.family']='Kaiti'\n",
    "plt.rcParams['font.size']=14\n",
    "plt.legend(['低体重','正常','超重','肥胖'],title= '男女生体重指数',loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
